Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions
In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drou...
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ftdatacite:10.6084/m9.figshare.12854060 2023-05-15T17:33:08+02:00 Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions Amini, Mohsen Ghadami, Mohammad Fathian, Farshad Modarres, Reza 2020 https://dx.doi.org/10.6084/m9.figshare.12854060 https://tandf.figshare.com/articles/journal_contribution/Teleconnections_between_oceanic_atmospheric_indices_and_drought_over_Iran_using_quantile_regressions/12854060 unknown Taylor & Francis https://dx.doi.org/10.1080/02626667.2020.1802029 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences Science Policy 69999 Biological Sciences not elsewhere classified Inorganic Chemistry FOS Chemical sciences Text article-journal Journal contribution ScholarlyArticle 2020 ftdatacite https://doi.org/10.6084/m9.figshare.12854060 https://doi.org/10.1080/02626667.2020.1802029 2021-11-05T12:55:41Z In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions. Text North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) Soi ENVELOPE(30.704,30.704,66.481,66.481) |
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59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences Science Policy 69999 Biological Sciences not elsewhere classified Inorganic Chemistry FOS Chemical sciences |
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59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences Science Policy 69999 Biological Sciences not elsewhere classified Inorganic Chemistry FOS Chemical sciences Amini, Mohsen Ghadami, Mohammad Fathian, Farshad Modarres, Reza Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
topic_facet |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences Ecology FOS Biological sciences Science Policy 69999 Biological Sciences not elsewhere classified Inorganic Chemistry FOS Chemical sciences |
description |
In this research, the Bayesian quantile regression model is applied to investigate the teleconnections between large oceanic–atmospheric indices and drought standardized precipitation index (SPI) in Iran. The 12-month SPI time series from 138 synoptic stations for 1952–2014 were selected as the drought index. Three oceanic–atmospheric indices, the North Atlantic Oscillation (NAO), the Southern Oscillation Index (SOI) and the Multivariate El Niño/Southern Oscillation Index (MEI), were selected as covariates. The results show that NAO has the weakest impact on drought in different quantiles and different regions in Iran. La Niña conditions amplified droughts through all SPI quantiles in western, Caspian Sea coastal regions and southern regions. The positive phase of MEI significantly modulates low SPI quantiles (i.e. drought conditions) throughout the Zagros region, Caspian Sea coastal regions and southern regions. The study shows that the effect of large oceanic–atmospheric indices have heterogeneous impacts on extreme dry and wet conditions. |
format |
Text |
author |
Amini, Mohsen Ghadami, Mohammad Fathian, Farshad Modarres, Reza |
author_facet |
Amini, Mohsen Ghadami, Mohammad Fathian, Farshad Modarres, Reza |
author_sort |
Amini, Mohsen |
title |
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
title_short |
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
title_full |
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
title_fullStr |
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
title_full_unstemmed |
Teleconnections between oceanic–atmospheric indices and drought over Iran using quantile regressions |
title_sort |
teleconnections between oceanic–atmospheric indices and drought over iran using quantile regressions |
publisher |
Taylor & Francis |
publishDate |
2020 |
url |
https://dx.doi.org/10.6084/m9.figshare.12854060 https://tandf.figshare.com/articles/journal_contribution/Teleconnections_between_oceanic_atmospheric_indices_and_drought_over_Iran_using_quantile_regressions/12854060 |
long_lat |
ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Soi |
geographic_facet |
Soi |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://dx.doi.org/10.1080/02626667.2020.1802029 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.12854060 https://doi.org/10.1080/02626667.2020.1802029 |
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1766131532034998272 |